The Distributional Impact of Climate, Employment, and Social Vulnerabilities¹

The analysis of the distributional impact of fiscal and social reforms is often disassociated from climate adaptation reforms. At the same time, the distributional impact of climate adaptation reforms is seldomly performed at the subnational level, especially in developing countries. This analysis aims at marrying these two dimensions for Ecuador by constructing the spatial distribution of climate, economic, and social vulnerabilities.

1. The COVID-19 pandemic and subsequent economic crisis have disproportionally affected low-income families, increased poverty headcount, and exacerbated income inequality in Ecuador. The ongoing expansion of social assistance programs—to reach 80 percent of families in the three lowest income deciles by end-2021—is partly offsetting the economic downturn for vulnerable families and containing income inequality. The fiscal savings from the phase out of regressive fuel subsidies are being channeled towards targeted cash transfers and other critical public spending.²

2. While positive at the aggregate level, policies to foster a transition to a greener economy are likely to have heterogeneous local economic effects. The international community has advocated that countries have the opportunity to foster a green and equitable recovery (see, e.g., Allan and others 2020, Coalition of Finance Ministers for Climate Action 2020).³ Environmental-friendly policies include increasing the share of renewable sources in the energy mix, retrofitting buildings, increasing forestation and carbon sequestration, and moving away from carbon-intensive industrial processes. These policies, however, would disproportionally affect some localities, given the geographical concentration of activities with high green-house gases footprint (see forthcoming IMF’s Regional Economic Outlook for the Western Hemisphere). While greening the economy indeed seems to be globally welfare-enhancing, it is arguably Pareto inefficient between countries (i.e., it is Hicks-Kaldor efficient with transfers). Climate policies likely have similar distributional implications between constituencies and income groups within countries.

3. Policies on greening the recovery therefore ought to be carefully designed to avoid backlash. While clean-energy infrastructure is labor intensive in the short term (Garrett-Peltier 2017), not all green investments create jobs quickly (Popp and others 2020). Also, some forms of green investment are not job-rich in the long term and require specific skills: for example, windmills are capital intensive and produced in only a few countries.

4. This study assesses the configuration of climate change, employment, and social protection in Ecuador across geographical districts. It constructs composite indices of climate, employment, and social vulnerabilities at the province-level and normalized as z-scores—i.e., the observed value minus the mean, divided by the standard deviation. The normalized variables have a mean of zero and a standard deviation of one, making the different indices comparable.⁴ The indices are as follow:

• Climate vulnerability. The climate vulnerability index is the average of the z-score of vulnerability to extreme temperatures, droughts, and fires and the z-score of vulnerability to extreme rains, floods, and mass movement from the World Bank’s (2021) report. Map 1 plots the climate vulnerability by province. Carchi, Loja, Napo, Pastaza, and Zamora Chinchipe are the most vulnerable to climate, followed by El Oro, Imbabura, Orellana, and Sucumbios. I.e., generally, the Amazonia region provinces and the border provinces in the Sierra region are historically more vulnerable to extreme climate events.

• Employment vulnerability. Employment opportunities in Ecuador are unequally distributed across provinces. The employment vulnerability index for each province is computed as the z-score of the sum of the unemployment and inadequate employment rates: [(1 – employed/labor_force) + employed x (1 – adequate_employment)). The employment data comes from the labor survey at the household level conducted quarterly by the Instituto Nacional de Estadística y Censos (INEC). We use December 2019 as representing the pre-pandemic period. Map 2 plots the employment vulnerability index by province. Cotopaxi, Chimborazo, Napo, and Zamora Chinchipe have the highest employment vulnerability, followed by Bolivar, Carchi, Morona Santiago, Pastaza, and Tungurahua.

• Social vulnerability. While Ecuador has incremented the social protection programs to the lowest three income deciles, the evolution of coverage of cash transfers has been uneven across provinces. The social vulnerability index is computed as the z-score of the share of the poor population not covered by social protection. Due to the lack of geographical data on the distribution of social protection for the bottom three deciles, the social vulnerability index was proxied as the share of the poor population not receiving the two major cash transfers—i.e., “Bono de Desarrollo Humano” (BDH) and “Bono de Desarrollo Humano con Componente Variable” (BVA)—weighted by the share of these transfers to the bottom three income deciles: [1 – (0.78 x BDH + 0.95 x BVA)/(population x poverty_rate)). The data on BDH, BVA, and their weights comes from the Ministry of Economic and Social Inclusion (MIES). Map 3 plots the social vulnerability index by province. Chimborazo, Pichincha, Pastaza, and Sucumbios stand out as socially vulnerability, followed by Carchi, Esmeraldas, Morona Santiago, Santa Elena, and Zamora Chinchipe. The uneven distribution of social protection is due to the limited updating of the social registry for regions that are hard to access (e.g., Pastaza and Sucumbios) and some regions enlarging “urban pockets of poverty” (e.g., around Quito in Pichincha and around Riobamba in Chimborazo).

• Policy options. The analysis highlights that the vulnerability to climate events, labor markets fragility, and weak social protection are significant and partly overlap across locations (see Table 1 with cross-correlations). Map 4 presents a compound “heat map” constructed as the unweighted sum of the ranks of climate, employment, and social vulnerability indices and Figure 1 plots the analyzed vulnerability dimensions. Carchi, Pastaza, and Zamora Chinchipe stand out as triple down: susceptible to extreme climate events, fragile employment, and weak social protection coverage. To ameliorate the climate and employment vulnerabilities, indicative targets of social protection coverage could include a location-specific floor: for example, no less than 80 percent coverage by province.

Ecuador: Climate, Employment, and Social Vulnerabilities by Province

Citation: IMF Staff Country Reports 2021, 229; 10.5089/9781513599274.002.A007

Source: World Bank, INEC, MIES and authors’ calculation.

• Download Figure
• Download figure as PowerPoint slide

View Full Size

Ecuador: Climate, Employment, and Social Vulnerabilities by Province

Citation: IMF Staff Country Reports 2021, 229; 10.5089/9781513599274.002.A007

Source: World Bank, INEC, MIES and authors’ calculation.

• Download Figure
• Download figure as PowerPoint slide

Ecuador: Climate, Employment, and Social Vulnerabilities by Province

Citation: IMF Staff Country Reports 2021, 229; 10.5089/9781513599274.002.A007

Source: World Bank, INEC, MIES and authors’ calculation.

• Download Figure
• Download figure as PowerPoint slide

^{Table 1.}

Ecuador: Cross-Correlations of Climate, Employment, and Social Vulnerability Indices by Province

Source: Author’s calculations.

^{Table 1.}

Ecuador: Cross-Correlations of Climate, Employment, and Social Vulnerability Indices by Province

	Climate vulnerability	Employment vulnerability	Social vulnerability
Climate vulnerability	1.00
Employment vulnerability	0.39**	1.00
Social vulnerability	0.34*	0.22	1.00

Source: Author’s calculations.

View Table

^{Table 1.}

Ecuador: Cross-Correlations of Climate, Employment, and Social Vulnerability Indices by Province

	Climate vulnerability	Employment vulnerability	Social vulnerability
Climate vulnerability	1.00
Employment vulnerability	0.39**	1.00
Social vulnerability	0.34*	0.22	1.00

Source: Author’s calculations.

Ecuador: Scatter Plot of Climate, Employment, and Social Vulnerability Indices by Province

Citation: IMF Staff Country Reports 2021, 229; 10.5089/9781513599274.002.A007

Source: Authors’ calculations.

• Download Figure
• Download figure as PowerPoint slide

View Full Size

Ecuador: Scatter Plot of Climate, Employment, and Social Vulnerability Indices by Province

Citation: IMF Staff Country Reports 2021, 229; 10.5089/9781513599274.002.A007

Source: Authors’ calculations.

• Download Figure
• Download figure as PowerPoint slide

Ecuador: Scatter Plot of Climate, Employment, and Social Vulnerability Indices by Province

Citation: IMF Staff Country Reports 2021, 229; 10.5089/9781513599274.002.A007

Source: Authors’ calculations.

• Download Figure
• Download figure as PowerPoint slide

5. Future analysis could harness more elaborated approaches and data for a detailed spatial and income analysis. For example:

• Employment transition matrixes of greening the economy by provinces and cantons (i.e., simulated employment shifts by industrial codes and geographical location) accompanied by fiscal schemes for compensating the losing constituencies (e.g., social assistance, training, carbon taxes, abatement technologies, and clean energy subsidies; see: Schaffitzel, Jakob, Soria, Vogt-Schilb, and Ward 2020).

• A dynamic economic assessment model of the world economy with a high spatial 1×1-degree resolution to determine the impact of temperature changes in productivity and amenities depending on local temperatures (cf. Alvarez and Rossi-Hansberg 2021) in Ecuador, and Colombia and Peru for comparison and range validation.

• The IMF’s Carbon Pricing Assessment Tool (CPAT) allows for the estimation of various impacts of policies to accelerate green transitions, notably carbon pricing, on various metrics, including distributional impacts (see upcoming WHD REO for application to selected Latin American countries).